Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Name: Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Brand: Springer-Verlag New York Inc.
SKU: 9780387712642
Price: 943.07 RON
Availability: InStock

Autor: Scott M. Lynch

Limbă:

Legare: Copertă tare

Editura: Springer-Verlag New York Inc.

Disponibilitate: În depozitul extern

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943.07 lei

"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the comple...

Informații despre carte

Autor

Scott M. Lynch

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2007

Pagini

359

EAN

9780387712642

ISBN

038771264X

Enbook ID

01382443

Editura

Springer-Verlag New York Inc.

Greutate

730

Dimensiuni

165 x 241 x 24

Descriere completă

"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.§The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

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